A Two-Stage Multistep-Ahead Electricity Load Forecasting Scheme Based on LightGBM and Attention-BiLSTM
- Authors
- Park, Jinwoong; Hwang, Eenjun
- Issue Date
- 11월-2021
- Publisher
- MDPI
- Keywords
- attention mechanism; electricity load forecasting; light gradient boosting machine; multistep-ahead forecasting; smart grid
- Citation
- SENSORS, v.21, no.22
- Indexed
- SCIE
SCOPUS
- Journal Title
- SENSORS
- Volume
- 21
- Number
- 22
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/135968
- DOI
- 10.3390/s21227697
- ISSN
- 1424-8220
- Abstract
- An efficient energy operation strategy for the smart grid requires accurate day-ahead electricity load forecasts with high time resolutions, such as 15 or 30 min. Most high-time resolution electricity load prediction techniques deal with a single output prediction, so their ability to cope with sudden load changes is limited. Multistep-ahead forecasting addresses this problem, but conventional multistep-ahead prediction models suffer from deterioration in prediction performance as the prediction range is expanded. In this paper, we propose a novel two-stage multistep-ahead forecasting model that combines a single-output forecasting model and a multistep-ahead forecasting model to solve the aforementioned problem. In the first stage, we perform a single-output prediction based on recent electricity load data using a light gradient boosting machine with time-series cross-validation, and feed it to the second stage. In the second stage, we construct a multistep-ahead forecasting model that applies an attention mechanism to sequence-to-sequence bidirectional long short-term memory (S2S ATT-BiLSTM). Compared to the single S2S ATT-BiLSTM model, our proposed model achieved improvements of 3.23% and 4.92% in mean absolute percentage error and normalized root mean square error, respectively.
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Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
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